3,103 research outputs found
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients
Federated optimization, an emerging paradigm which finds wide real-world
applications such as federated learning, enables multiple clients (e.g., edge
devices) to collaboratively optimize a global function. The clients do not
share their local datasets and typically only share their local gradients.
However, the gradient information is not available in many applications of
federated optimization, which hence gives rise to the paradigm of federated
zeroth-order optimization (ZOO). Existing federated ZOO algorithms suffer from
the limitations of query and communication inefficiency, which can be
attributed to (a) their reliance on a substantial number of function queries
for gradient estimation and (b) the significant disparity between their
realized local updates and the intended global updates. To this end, we (a)
introduce trajectory-informed gradient surrogates which is able to use the
history of function queries during optimization for accurate and
query-efficient gradient estimation, and (b) develop the technique of adaptive
gradient correction using these gradient surrogates to mitigate the
aforementioned disparity. Based on these, we propose the federated zeroth-order
optimization using trajectory-informed surrogate gradients (FZooS) algorithm
for query- and communication-efficient federated ZOO. Our FZooS achieves
theoretical improvements over the existing approaches, which is supported by
our real-world experiments such as federated black-box adversarial attack and
federated non-differentiable metric optimization
Distributed Information Retrieval using Keyword Auctions
This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions
RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction
Robots have potential to revolutionize the way we interact with the world
around us. One of their largest potentials is in the domain of mobile health
where they can be used to facilitate clinical interventions. However, to
accomplish this, robots need to have access to our private data in order to
learn from these data and improve their interaction capabilities. Furthermore,
to enhance this learning process, the knowledge sharing among multiple robot
units is the natural step forward. However, to date, there is no
well-established framework which allows for such data sharing while preserving
the privacy of the users (e.g., the hospital patients). To this end, we
introduce RoboChain - the first learning framework for secure, decentralized
and computationally efficient data and model sharing among multiple robot units
installed at multiple sites (e.g., hospitals). RoboChain builds upon and
combines the latest advances in open data access and blockchain technologies,
as well as machine learning. We illustrate this framework using the example of
a clinical intervention conducted in a private network of hospitals.
Specifically, we lay down the system architecture that allows multiple robot
units, conducting the interventions at different hospitals, to perform
efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure
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